Abstract
Document alignment and registration play a crucial role in numerous real-world applications, such as automated form processing, anomaly detection, and workflow automation. Traditional methods for document alignment rely on image-based features like keypoints, edges, and textures to estimate geometric transformations, such as homographies. However, these approaches often require access to the original document images, which may not always be available due to privacy, storage, or transmission constraints. This paper introduces a novel approach that leverages Optical Character Recognition (OCR) outputs as features for homography estimation. By utilizing the spatial positions and textual content of OCR-detected words, our method enables document alignment without relying on pixel-level image data. This technique is particularly valuable in scenarios where only OCR outputs are accessible. Furthermore, the method is robust to OCR noise, incorporating RANSAC to handle outliers and inaccuracies in the OCR data. On a set of test documents, we demonstrate that our OCR-based approach even performs more accurately than traditional image-based methods, offering a more efficient and scalable solution for document registration tasks. The proposed method facilitates applications in document processing, all while reducing reliance on high-dimensional image data.
Abstract (translated)
文档对齐和注册在诸如自动化表单处理、异常检测以及工作流自动化等众多实际应用场景中发挥着关键作用。传统的文档对齐方法依赖于基于图像的特征，如关键点、边缘和纹理来估计几何变换（例如仿射变换）。然而，这些方法通常需要访问原始文档图像，但由于隐私保护、存储或传输限制等原因，这种情况并不总是可行。 本文介绍了一种新颖的方法，该方法利用光学字符识别（OCR）输出作为特征来进行仿射变换的估算。通过利用OCR检测到的文字的空间位置和文本内容，我们的方法能够在不依赖像素级图像数据的情况下实现文档对齐。在只有OCR输出可用的情境下，这种技术特别有价值。 此外，我们提出的方法具有鲁棒性，能够处理OCR噪声，并结合RANSAC算法来应对OCR数据中的异常值和准确性问题。通过一组测试文件，我们证明了基于OCR的方法甚至比传统的基于图像的方法表现出更高的精度，提供了一种更高效且可扩展的文档注册解决方案。 该方法促进了文档处理的应用发展，并减少了对高维图像数据的依赖。
URL
https://arxiv.org/abs/2505.18925
